ErLinkTopic: A generative probabilistic framework for analyzing regional communities in social networks

Abstract

Understanding how communities evolve over time have become a hot topic in the field of social network analysis due to the wide range of its applications. In this context, several approaches have been introduced to capture changes in the community members. Our claim is that a community is characterized by not only the identity of users but complex features such as the topics of interest, and the regional and geographic characteristics. Studying changes in such features of communities also provides informative findings for related applications. This leads to the main goal of the study in this paper, which is to capture the evolution of complex features describing communities. Particularly, we introduce a probabilistic framework called ErLinkT opic model. The model is able to extract regional LinkT opic [1] communities and to capture gradual changes in three features describing each community, i.e., community members, the prominence of topics describing communities, and terms describing such topics. It further supports the study of regional and geographic characteristics of communities as well as changes in such features. Experimental evaluations have been conducted using Twitter data to evaluate the model in terms of its effectiveness and efficiency in extracting communities and capturing changes in the features describing each community